Artificial intelligence as a service (AIaaS) refers to off-the-shelf AI tools that allow businesses to implement and scale AI techniques at a fraction of the cost of a full-fledged in-house AI.
Because it is based on cloud computing, the concept of everything as a service refers to any software that can be accessed across a network. In most cases, the software is readily available. You buy it from a third-party vendor, make a few changes, and start using it almost immediately, even if it hasn’t been completely customized for your system.
For a long time, most businesses found artificial intelligence to be prohibitively expensive:
The machines were enormous and costly.
There was a scarcity of programmers who worked on such machines (which meant they demanded high payments).
Many businesses lacked the necessary data to conduct research.
AI has become more accessible as cloud services have become more widely available: businesses can now collect and store an infinite amount of data. This is where AI-as-a-service enters the picture.
Let’s take a detour into AI now so that we have the right expectations when working with AIaaS.
What is AI
We hear it repeated over and over: artificial intelligence is a way to get machines to do the same kind of work that human brains can accomplish. This definition is the subject of significant debate, with technology experts arguing that comparing machines to human brains is the wrong paradigm to use. It may promote fear that humans can be taken over by machines.
The term AI can also be used as a marketing tactic for companies to show how innovative they are—something known as artificial AI or fake AI.
Artificial intelligence is defined as machines that can adapt to new environments and solve new problems. Computers, like humans, are constantly reacting to new challenges, and they can now react in ways that their programmers did not explicitly program them for.
Importantly, AI is not created by itself; rather, it is created by humans. If an entity can do things that humans do, we call it intelligent.
Machine learning is the dominant type of AI today. It is the most developed of several areas of artificial intelligence. However, much like AI, there is a lot of hype surrounding ML versus what it actually is. Today, machine learning can do a lot of things, but it isn’t a magic bullet that will solve all of your organizational problems.
AIaaS is growing in popularity.
AIaaS is the solution for businesses that are unable or unwilling to build their own clouds and build, test, and deploy their own artificial intelligence systems. This is the most appealing feature: the ability to benefit from data insights without requiring a large upfront investment in talent and resources.
AIaaS, like other “as a service” options, offers the following advantages:
Maintaining focus on the core business (not becoming data and machine learning experts)
Keeping investment risk to a minimum
Increasing the value you derive from your data
Increasing strategic adaptability
Increasing cost flexibility and transparency
Types of AIaaS
The following are examples of AIaaS:
Chatbots and digital help
Chatbots that use natural language processing (NLP) algorithms to learn from human conversations and imitate language patterns while providing answers are examples of this. This allows customer service representatives to focus on more difficult tasks.
These are the most common types of AIaaS today.
Cognitive computing APIs
Short for application programming interface, APIs are a way for services to communicate with each other. APIs allow developers to add a specific technology or service into the application they are building without writing the code from scratch. Common options for APIs include:
Natural Language Processing (NLP)
Computer speech and computer vision
Translation
Knowledge mapping
Search
Emotion detection
Frameworks for machine learning
ML and AI frameworks are tools that developers can use to create their own model that learns from existing company data over time.
Machine learning is frequently associated with big data, but it can have other applications—and these frameworks provide a way to incorporate machine learning tasks without requiring a big data environment.
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